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Record W2623305101 · doi:10.1080/19942060.2017.1292410

Solving incompressible fluid flows on unstructured meshes with the lattice Boltzmann flux solver

2017· article· en· W2623305101 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEngineering Applications of Computational Fluid Mechanics · 2017
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsLattice Boltzmann methodsPolygon meshSolverTurbulenceAirfoilMechanicsComputational fluid dynamicsCompressibilityIncompressible flowPhysicsBoundary value problemBoundary layerGeometryMathematical optimizationMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

An application of the recently developed lattice Boltzmann flux solver (LBFS) is proposed to solve incompressible flows using unstructured meshes with high aspect ratio triangular cells. The capability to solve turbulent flows is also introduced by coupling the method with a turbulence model, for which the viscosity transport equation is solved on the same mesh. The proposed computational approach is validated for the classical lid-driven cavity flow, the flow over a circular cylinder, and the turbulent flow around a NACA0012 airfoil. Overall, the results obtained agree well with reference data, and demonstrate the validity of using the LBFS on directionally refined meshes, providing the advantage of limiting the number of vertices required in boundary layer regions of the fluid flow. An alternate flux construction method derived from a lattice Boltzmann boundary condition model based on equilibrium distribution streaming is also presented.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.905
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.231
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it